Apparatus and method for performing adaptive channel estimation in a mobile communication system

ABSTRACT

Disclosed is an adaptive channel estimator for improving a performance of a general channel estimator in a mobile communication system, and a method for controlling the same. The adaptive channel estimator further detects a noise level of a channel and a channel speed, and implements an optimum noise elimination filter on the basis of the detected noise level and channel speed. A comparison between mapping degrees predetermined by the detected noise level and channel speed allows an optimum noise elimination filter to be implemented, If such a channel estimator is implemented, then optimum packet data transmission is available for not only a low-speed channel but also a high-speed channel. A channel compensation caused by a difference between a spreading factor (SF) of a pilot channel and a spreading factor (SF) of a data channel can be compensated on the condition that a slope compensator executed by a SF ratio is controlled by a filter coefficient controller.

PRIORITY

[0001] This application claims priority to an application entitled“APPARATUS AND METHOD FOR PERFORMING ADAPTIVE CHANNEL ESTIMATION INMOBILE COMMUNICATION SYSTEM”, filed in the Korean Industrial PropertyOffice on Jul. 9, 2002 and assigned Serial No. 2002-39847, the contentsof which are incorporated herein by reference.

CROSS REFERENCE TO RELATED APPLICATION

[0002] Related subject matter is disclosed in U.S. Non-Provisionalapplication of Hun-Kee Kim et al., filed on ______, entitled “AdaptiveTransmit Antenna Diversity Apparatus And Method In A MobileCommunication System”, the contents of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

[0003] 1. Field of the Invention

[0004] The present invention relates to an apparatus and method forperforming channel estimation in a mobile communication system, and moreparticularly to an apparatus and method for performing adaptive channelestimation according to wireless channel environments in a mobilecommunication system.

[0005] 2. Description of the Related Art

[0006] Mobile communication systems have evolved from providing a userwith voice signals to providing the user with high-speed andhigh-quality wireless data packets, allowing users to use a variety ofdata services and multimedia services. A third-generation mobilecommunication system classified as a 3GPP (3rd Generation PartnershipProject) is an asynchronous system, and a 3GPP2 is a synchronous system.Both 3^(rd) generation systems are being standardized to implementhigh-speed and high-quality wireless packet communication services. Forexample, a HSDPA (High Speed Downlink Packet Access) standardization isin progress for the 3GPP, and a 1×EV-DV standardization is in progressfor the 3GPP2. Standardization is needed for users or subscribers toreceive high-speed (more than 2 Mbps) and high-quality wireless datapacket transmission service in the third-generation mobile communicationsystem. A fourth-generation mobile communication system is needed forusers or subscribers to receive higher-speed and higher-qualitymultimedia communication services.

[0007] Typically, the HSDPA scheme is a specific data transfer schemefor processing a HS-DSCH (High Speed-Downlink Shared Channel) and itsassociated control channels. The HS-DSCH is a downlink data channel forsupporting a downlink high-speed packet data transmission service in anasynchronous UMTS (Universal Mobile Terrestrial System) mobilecommunication system.

[0008] The HSDPA scheme requires general techniques used for aconventional mobile communication system, and other advanced techniquesfor improving channel environment adaptability. Recently, three schemeshave been developed for supporting a high-speed packet transmissionservice in the HSDPA, i.e., an AMCS (Adaptive Modulation and CodingScheme), a n-channel SAW (Stop And Wait) HARQ (Hybrid Automatic RepeatRequest) scheme, and a FCS (Fast Cell Selection) scheme.

[0009] Firstly, the AMCS determines modulation and coding methods of adata channel according to a channel status between a cell and a user.This results in increased channel usage efficiency of overall cells. Acombined scheme between the modulation method and the coding method iscalled a MCS (Modulation and Coding Scheme), and can be defined as aplurality of MCSs ranging from a level “1” to a level “n”. The AMCSadaptively determines individual levels of the MCSs according to achannel status between a user and a cell. This results in increasedoverall channel usage efficiency.

[0010] Secondly, the n-channel SAW HARQ scheme functioning as one ofmany HARQ schemes successively transmits a plurality of packets eventhough an ACK (ACKnowledgement) signal is not received, resulting inincreased channel usage efficiency. In other words, provided that Nlogic channels are set up between a UE (User Equipment) and a Node-B,and these N logic channels can be identified by a specific time or aspecific channel number, the UE serving as a reception end can recognizewhich one of the channels contains a packet received at a predeterminedtime. Further, the UE can reconstruct packets in the order of reception.

[0011] Thirdly, the FCS scheme receives packets from a cell whichmaintains the best channel status when the UE using the HSDPA enters asoft handover region. This results in reduced overall interferencebetween channels. If the cell for providing a user with the best channelstatus is changed to a new cell, the FCS scheme performs packettransmission using a HS-DSCH of this new cell. When performing suchpacket transmission, there is a need for the FCS scheme to minimizetransmission discontinuity time.

[0012] The AMCS contained in the aforementioned three high-speed packettransmission services will hereinafter be described in more detail.

[0013] There are a variety of modulation/demodulation schemes beingcurrently investigated for the AMCS, for example, a QPSK (QuadraturePhase Shift Keying), a 8PSK, and a 16QAM (16 Quadrature AmplitudeModulation), etc. A variety of code rates ranging from “¼” to “1” arebeing considered by many developers as a coding scheme. Therefore, amobile communication system using the AMCS provides high-ordermodulation/demodulation schemes (e.g., 8PSK, and 16QAM) and a high coderate to UEs (e.g., UEs located in the vicinity of a Node-B) assigned toa good channel, whereas it provides a low-order modulation scheme suchas a QPSK and a low code rate to UEs (e.g., UEs located at a cellboundary) assigned to a relatively poor channel. Rather than use theQPSK, it is possible for the QPSK serving as a low-order modulationscheme to perform channel estimation using a phase prediction function,because the QPSK contains one symbol for every quadrant with respect tothe constellation. However, two or four symbols for every quadrant areprovided for the 8PSK and 16QAM serving as a high-order modulationscheme. Specifically, several symbols having different amplitudes arepositioned in the same phase region, such that not only phase estimationbut also channel estimation based on precise amplitude information isneeded.

[0014] In the meantime, the utilization of the high-order modulationscheme for performing high-speed and high-quality data services and thehigh code rate is mainly restricted due to a variety of wireless channelenvironments, for example, a white noise, a variation in signalreception power levels due to the fading phenomenon, a shadowingoccurrence, a Doppler effect caused by UE's mobility and UE's frequentspeed change, and a signal interference caused by either another user ormultipath signals, etc. Therefore, a mobile communication systemrequires appropriate modulation/coding schemes according to wirelesschannel environments which vary according to the above mentionedfactors. A receiver of the mobile communication system should have achannel estimator functioning as an additional signal compensator forchanging reception signals undesirably distorted by the above mentionedfactors to original signals.

[0015] Typically, a conventional mobile communication system adaptspilot signals to predict such wireless channel environments.Specifically, a Node-B transmits pilot signals over a common pilotchannel such as a PICH or CPICH. All UEs contained in a given area ofthe Node-B receive the pilot signals from the Node-B, and predict thewireless channel environments such as the fading phenomenon using thereceived pilot signals. However, it is difficult for the above method topredict a wireless channel environment change caused by random whitenoise characteristics. To solve this problem, the channel estimatorincludes a noise elimination filter for smoothing random characteristicsof the white noise, such that the noise can be considerably reduced. Forexample, IIR (Infinite Impulse Response) filter is mainly adapted as thenoise elimination filter, and is suitable for a mobile communicationsystem adapting a QPSK as a modulation scheme.

[0016]FIG. 1 is a block diagram of an example of a channel estimator foruse in a conventional mobile communication system. FIG. 2 is a blockdiagram illustrating an example of a channel estimator for use in areceiver of a conventional mobile communication system. The channelestimator shown in FIG. 1 includes a first integration/dump filter 110,a second integration/dump filter 130, a complex conjugate patterngenerator 120, and a noise elimination filter 140.

[0017] Referring to FIG. 1, input signals IN are provided to the firstintegration/dump filter 110. The input signals may be pilot signalsreceived from common pilot channels, for example. The firstintegration/dump filter 110 accumulates the input signals (i.e., thepilot signals) in response to a SF (Spreading Factor) used for thecommon pilot channels, and numerically integrates the accumulated inputsignals. A measurement detected by the first integration/dump filter 110may be reception intensity in symbol units in association with the inputsignals. This measurement is provided to a multiplier 150.

[0018] The complex conjugate pattern generator 120 generates a complexconjugate pattern corresponding to a symbol pattern of a pilot signaltransmitted over the common pilot channel. The complex conjugate patterngenerated from the complex conjugate pattern generator 120 is applied tothe multiplier 150. The multiplier 150 multiplies the complex conjugatepattern generated from the complex conjugate pattern generator 120 bythe measurement generated from the first integration/dump filter 110,and thus generates a subdivided signal corresponding to a desiredantenna. The subdivided signal associated with the desired antenna isreceived from the multiplier 150, and is transmitted to the secondintegration/dump filter 130. The second integration/dump filter 130receives subdivided signals for desired antennas from the multiplier150, accumulates the subdivided signals in two-symbol units, numericallyintegrates the accumulated signals, and thus outputs a channelprediction value. But, this channel prediction value does not considerthe white noise contained in the input signals IN. Therefore, thechannel prediction value is transmitted to the noise elimination filter140 to obtain a more accurate value which considers the white noise. Thenoise elimination filter 140 then removes the white noise componentcontained in the channel prediction value from an output signal of thesecond integration/dump filter 130, and thus generates a correct channelprediction value. The IIR filter may be adapted as the noise eliminationfilter 140.

[0019] A detailed circuit diagram of the IIR filter functioning as thenoise elimination filter 130 is shown in FIGS. 2 and 3. FIG. 3 is adetailed block diagram of an example of an N-th IIR filter adapted asanother example of the noise elimination filter of FIG. 1. Specifically,FIG. 2 is a detailed circuit diagram of a primary IIR filter, and FIG. 3is a detailed circuit diagram of an N-th IIR filter.

[0020] I/o (Input/Output) characteristic of the primary IIR filter isrepresented by the following Equation 1:

[0021] [Equation 1]

y(n)=b·x(n)+a·y(n−1)

[0022] where y(n) is a current output signal, x(n) is a current inputsignal, and y(n−1) is a previous output signal.

[0023] With reference to the above Equation 1, the output signal “y(n)”is the sum of the input signal “x(n)” multiplied by a constant “b” andone-delayed output signal “y(n−1)” multiplied by the other constant “a”.

[0024] The I/O characteristic of the N-th IIR filter is represented bythe following Equation 2: $\begin{matrix}{{y(n)} = {{b \cdot {x(n)}} + {\sum\limits_{k = 1}^{N}{a_{k} \cdot {y\left( {n - k} \right)}}}}} & \text{[Equation~~2]}\end{matrix}$

[0025] As can be seen from the above Equation 2, the I/O characteristicof the N-th IIR filter considers first to N-th previous output signalsy(n−1), y(n−2), . . . , y(n−N). As shown in the above Equation 2, theoutput signal “y(n)” is the sum of the input signal “x(n)” multiplied bya constant “b” and individual delayed output signals (i.e., first toN-th delayed output signals) multiplied by the other constant “a”.

[0026] As can be seen from the above Equations 1 and 2, thecharacteristic of the IIR filter is determined by filter coefficients“a” and “b”. The filter coefficient “a” is a feedback weight, and theother coefficient “b” is an input weight.

[0027] Referring to FIG. 2, an input signal x(n) is provided to a firstmultiplier 210. The signal is multiplied by the first filter coefficient“b”, and is then transmitted to one input terminal of an adder 212. Theother input terminal of the adder 212 receives a signal a·y(n−1) derivedby multiplication of a previous output signal y(n−1) and a second filtercoefficient “a”. The adder 212 adds an output signal b·x(n) of the firstmultiplier 210 and the output signal a·y(n−1) of a second multiplier 216to create a result signal b·x(n)+a·y(n−1), resulting in a channelprediction value y(n) equal to the result signal b·x(n)+a·y(n−1). Thechannel prediction value y(n) is transmitted to a delay 214. The delay214 delays the received signal y(n) to provide a delayed signal y(n−1),and transmits the delayed signal y(n−1) to the second multiplier 216.

[0028] The IIR filter applies a previous output signal to a new inputsignal, and thus prevents its output signal from being abruptly changeddue to a white noise.

[0029] The frequency characteristic of the primary IIR filter shown inFIG. 2 is represented by the following Equation 3: $\begin{matrix}{{H\left( ^{j\quad w} \right)} = \frac{b}{1 - {a \cdot ^{{- j}\quad w}}}} & \text{[Equation~~3]}\end{matrix}$

[0030] A direct current (DC) gain of the primary IIR filter having theabove frequency characteristic shown in the above Equation 3 is aspecific value at a prescribed condition of ω=0 indicating a frequencyof “0”, such that the DC gain can be represented by the followingEquation 4: $\begin{matrix}{{{H(1)}} = \frac{b}{{1 - a}}} & \text{[Equation~~4]}\end{matrix}$

[0031] Therefore, with reference to the above Equation 4, the DC gain ata predetermined condition of |b|=|1−a| is normalized to “1”.

[0032] Referring to FIG. 3, an input signal x(n) is applied to a firstmultiplier 310. The input signal is multiplied by the first filtercoefficient “b”, and is then applied to an adder 312. The other inputterminal of the adder 312 receives signals a₁·y(n−1) to a_(N)·y(n−N)from N second multipliers 316-1 to 316-N. The received signals a₁·y(n−1)to a_(N)·y(n−N) are created by a multiplication previous output signalsy(n−1) to y(n−N) and second filter coefficients a₁ to a_(N). The adder312 adds an output signal b·x(n) of the first multiplier 310 and theoutput signals a₁·y(n−1) to a_(N)·y(n−N) of the second multipliers 316to create a result signal b·x(n)+a₁·y(n−1)+ . . . +a_(N)·y(n−N),resulting in a channel prediction value y(n) equal to the result signalb·x(n)+a_(N)·y(n−1)+ . . . +a_(N)−y(n−N). The channel prediction valuey(n) is transmitted to a plurality of delays 314-1 to 314-N to createdelayed signals y(n−1), y(n−2), . . . , y(n−N). In this case, the delays314-1 to 314-N have different delay values.

[0033] As stated above, the N-th IIR filter shown in FIG. 3 uses Nfeedback signals composed of first to N-th feedback signals, resultingin more precisely correcting a noise component contained in a desiredsignal.

[0034] The frequency characteristic of the N-th IIR filter shown in FIG.3 is represented by the following Equation 5: $\begin{matrix}{{H\left( ^{j\quad w} \right)} = {\frac{Y\left( ^{j\quad w} \right)}{X\left( ^{j\quad w} \right)} = \frac{b}{1 - {\sum\limits_{k = 1}^{N}{a_{k} \cdot ^{{- j}\quad w}}}}}} & \text{[Equation~~5]}\end{matrix}$

[0035] A DC gain of the N-th IIR filter having the above frequencycharacteristic shown in the above Equation 5 is represented by thefollowing Equation 6: $\begin{matrix}{{{H(1)}} = \frac{b}{{1 - {\sum\limits_{k = 1}^{N}a_{k}}}}} & \text{[Equation~~6]}\end{matrix}$

[0036] Therefore, with reference to the above Equation 6, the DC gain ata predetermined condition of${b} = {{1 - {\sum\limits_{k = 1}^{N}a_{k}}}}$

[0037] is normalized to “1”.

[0038] The channel estimator can be implemented with a plurality ofchannel estimators according to whether or not a Tx-diversity (i.e., atransmission diversity) is used. If the Tx-diversity is not used, thechannel estimator can be implemented with only one channel estimator.Otherwise, if the Tx-diversity is used, a plurality of channelestimators equivalent to the number of antennas used are needed.However, the channel estimator has the same configuration as FIG. 1,irrespective of the use of Tx-diversity. Referring to FIG. 1, a complexconjugate pattern generated from the complex conjugate pattern generator120 contained in the channel estimator may be one or more patternsaccording to the use of Tx-diversity. If Tx-diversity is provided, onlyone antenna is used, such that only one symbol pattern is generated. IfTx-diversity is adapted using a plurality of antennas, a plurality ofsymbol patterns are adapted to discriminate among the plurality ofantennas. The symbol patterns are adapted to discriminate among theantennas so as to separate each pilot signal from orthogonal pilotsignals for every antenna, allowing individual symbol patternsassociated with individual antennas to be orthogonal to each other.

[0039] In another example, individual channel estimators should beconfigured to be associated with individual antennas, one channelestimator selected from among many channel estimators should always beoperated, irrespective of the use of Tx-diversity. The remaining channelestimators other than the selected one channel estimator should beoperated only when they use the Tx-diversity.

[0040] Although a channel estimator having the IIR filter is suitablyused for a QPSK being a low-order modulation scheme, several problemsmay occur if a high-order modulation scheme is applied to the channelestimator. For example, a 16QAM being a high-order modulation scheme isvery sensitive to a noise problem as compared to QPSK, such that the16QAM is mainly used at a relatively high SNR (Signal to Noise Ratio).In conclusion, there is less necessity for the IIR filter in the 16QAMcompared with the QPSK. In more detail, a noise elimination filter maydeteriorate performance of the channel estimator in a wireless channelenvironment where there is a white noise lower than that of the QPSK.This problem of the noise elimination filter is called a laggingphenomenon, and is caused by characteristics of the IIR filter.

[0041] The following Table 1 shows examples of preferable coefficientvalues “a” and “b” which are adapted to the IIR filter according to theDoppler frequency and a moving speed of a mobile terminal (i.e., a UE).As shown in the Table 1, it is assumed that a chip rate is 3.84 Mcps,and a sample frequency f, for updating channel estimation in units of516 chips is 3.84 Mcps/512 chip, i.e., 7500 Hz. TABLE 1 A b = 1 − aCutoff frequency (3 dB) Transfer speed 1/4 3/4 2024 Hz 1093 km/h 1/2 1/2 862 Hz  465 km/h 3/4 1/4  346 Hz  197 km/h 7/8 1/8  159 Hz  86 km/h

[0042] Typically, a mobile communication system determines a movingspeed of a mobile terminal, and sets the filter coefficients “a” and “b”associated with the determined moving speed to fixed values,respectively. Specifically, the filter coefficients “a” and “b” for usein the IIR filter are respectively fixed to only one value correspondingto a specific moving speed of the mobile terminal, such that the IIRfilter can be operated by the fixed values “a” and “b” even though awireless channel environment is changed to another wireless channelenvironment, resulting in unexpected problems.

[0043] Such unexpected problems will hereinafter be described in moredetail with reference to FIGS. 4a and 4 b. A feedback signal (i.e., aprevious output signal) of the IIR filter shown in FIGS. 2 and 3 incursthe lagging phenomenon, because a current output signal of the IIRfilter is based on a previous output signal. The lagging phenomenon ismore critical to a wireless channel environment changing at a highspeed, and is more clearly shown in FIGS. 4a and 4 b. FIG. 4a is a graphillustrating an example of a lagging phenomenon of the IIR filter in aconventional high-speed fading channel. FIG. 4b is a graph illustratingan example of a lagging phenomenon of the IIR filter in a conventionallow-speed fading channel. As can be seen from FIGS. 4a and 4 b, thelagging phenomenon of FIG. 4a has a signal level higher than that ofFIG. 4b. Therefore, provided that a wireless channel environment isabruptly changed, there are large differences in predicted values of achannel estimator even though the same delay time is provided, such thatit is impossible for a reception signal to further reduce its own BER(Bit Error Rate).

[0044] To overcome the aforementioned disadvantages, the low-ordermodulation scheme is generally operated at a region of a low SNR lowerthan that of the high-order modulation scheme, and is more sensitive toa signal distortion than a signal amplitude, such that it is notaffected by the lagging phenomenon. However, if a high-order modulationscheme is used in a same way as in a HSDPA mobile communication system,the lagging phenomenon deteriorates the overall system performance.Therefore, a new method for solving the above problems is needed.

SUMMARY OF THE INVENTION

[0045] Therefore, it is an object of embodiments of the presentinvention to provide an apparatus and method for removing a noise from adesired signal by predicting information about a wireless channelenvironment.

[0046] It is another object of embodiments of the present invention toprovide an apparatus and method for adapting a high-order modulationscheme, and thereby preventing a system performance from beingdeteriorated due to a lagging phenomenon of a mobile communicationsystem.

[0047] It is yet another object of embodiments of the present inventionto provide a noise elimination filter for adaptively coping with alow-order modulation scheme and a high-order modulation scheme.

[0048] It is yet further another object of embodiments of the presentinvention to provide an apparatus and method for compensating for adifference between a SF (Spreading Factor) of a pilot channel and a SFof a data channel.

[0049] In accordance with an embodiment of the present invention, theabove and other objects can be accomplished by the provision of a methodfor determining first and second filter coefficients in a noiseelimination filter which receives a predicted channel response signaland the first and second filter coefficients where their sum is set to apredetermined value, and removes a noise component from the predictedchannel response signal, comprising the steps of: a) detecting a noiselevel upon receiving a difference between the predicted channel responsesignal and a previously predicted channel response signal, and detectingchannel speed prediction information upon receiving an auto-correlationfunction of the predicted channel response signal; and b) determiningfirst and second filter coefficients mapping-processed by the detectednoise level and the detected channel speed prediction information.

[0050] In accordance with another embodiment of the present invention,there is provided an apparatus for determining first and second filtercoefficients in a noise elimination filter which receives a predictedchannel response signal and the first and second filter coefficientswhere their sum is set to a predetermined value, and removes a noisecomponent from the predicted channel response signal, comprising: achannel-speed/noise-level detector for detecting a noise level uponreceiving a difference between the predicted channel response signal anda previously predicted channel response signal, and detecting channelspeed prediction information upon receiving an auto-correlation functionof the predicted channel response signal; and a filter coefficientcontroller for determining first and second filter coefficientsmapping-processed by the detected noise level and the detected channelspeed prediction information.

[0051] In accordance with yet another embodiment of the presentinvention, there is provided a method for receiving a common pilotchannel signal at an adaptive channel estimator of a mobilecommunication system, and removing a noise from the received commonpilot channel signal, comprising the steps of: a) multiplying a complexconjugate of a corresponding pilot symbol by the common pilot channelsignal, and outputting a predicted fading channel response signal; b)detecting a noise level contained in the predicted fading channelresponse signal; c) detecting a channel speed of the common pilotchannel signal on the basis of the predicted fading channel response; d)comparing the detected noise level with at least one first referencevalue; e) comparing the detected channel speed with at least one secondreference value; f) determining first and second filter coefficientsmapped with an area corresponding to the comparing result to be filtercoefficients for noise elimination, the first and second filtercoefficients being mapping-processed for every area assigned by thefirst and second reference values; and g) removing a noise componentfrom the predicted fading channel response signal using the determinedfirst filter coefficient and the determined second coefficient.

[0052] In accordance with yet further another embodiment of the presentinvention, there is provided an apparatus for receiving a common pilotchannel signal at an adaptive channel estimator of a mobilecommunication system, and removing a noise from the received commonpilot channel signal, comprising: a multiplier and an integration/dumpfilter for multiplying a complex conjugate of a corresponding pilotsymbol by the common pilot channel signal, and outputting a predictedfading channel response signal; a channel-speed/noise-level detector fordetecting a noise level contained in the predicted fading channelresponse signal, and detecting a channel speed of the common pilotchannel signal; a filter coefficient controller for setting first andsecond filter coefficients mapped with an area corresponding to thecomparing result to noise elimination filter coefficients, the first andsecond filter coefficients being mapping-processed for every areaassigned by the first and second reference values; and a noiseelimination filter for removing a noise component from the predictedfading channel response signal using the determined first filtercoefficient and the determined second coefficient.

BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and other advantages ofembodiments of the present invention will be more clearly understoodfrom the following detailed description taken in conjunction with theaccompanying drawings, in which:

[0054]FIG. 1 is a block diagram of an example of a channel estimator foruse in a conventional mobile communication system;

[0055]FIG. 2 is a detailed block diagram of an example of a primary IIRfilter adapted as an example of a noise elimination filter of FIG. 1;

[0056]FIG. 3 is a detailed block diagram of an example of an N-th IIRfilter adapted as another example of the noise elimination filter ofFIG. 1;

[0057]FIG. 4a is a graph illustrating an example of a lagging phenomenonof the IIR filter in a conventional high-speed fading channel;

[0058]FIG. 4b is a graph illustrating an example of a lagging phenomenonof the IIR filter in a conventional low-speed fading channel;

[0059]FIG. 5 is a block diagram of an example of a channel estimator inaccordance with an embodiment of the present invention;

[0060]FIG. 6a is a graph illustrating an example of a difference betweena plurality of values adjacent to each other in a high-speed channelwhen a general channel estimation is performed in accordance with anembodiment of the present invention;

[0061]FIG. 6b is a graph illustrating an example of a difference betweena plurality of values adjacent to each other in a low-speed channel whena general channel estimation is performed in accordance with anembodiment of the present invention;

[0062]FIG. 7a is a graph illustrating an example of a SNR associatedwith a single-level reference value and a SNR associated with the amountof noise of a detected pilot channel in accordance with an embodiment ofthe present invention;

[0063]FIG. 7b is a graph illustrating an example of a channel speedassociated with a single-level reference value and a channel speedassociated with a detected channel speed prediction parameter inaccordance with an embodiment of the present invention;

[0064]FIG. 8 is a graph illustrating an example of a mapping example offilter coefficients corresponding to a plurality of measurements when asingle-level reference value is adapted to the present invention inaccordance with an embodiment of the present invention;

[0065]FIG. 9a is a graph illustrating an example of a SNR associatedwith a multi-level reference value and a SNR associated with the amountof noise of a detected pilot channel in accordance with an embodiment ofthe present invention;

[0066]FIG. 9b is a graph illustrating an example of a channel speedassociated with a multi-level reference value and a channel speedassociated with a detected channel speed prediction parameter inaccordance with an embodiment of the present invention;

[0067]FIG. 10 is a graph illustrating an example of a mapping example offilter coefficients corresponding to a plurality of measurements whenmulti-level reference values are adapted to the present invention inaccordance with an embodiment of the present invention;

[0068]FIG. 11 is a graph illustrating an example where an independentreference value is adapted to determine whether a slope is compensatedor not in accordance with an embodiment of the present invention;

[0069]FIG. 12 is a graph illustrating an example of a principle of theslope compensation in accordance with an embodiment of the presentinvention;

[0070]FIG. 13 is a flow chart illustrating an example of operations ofthe channel estimator in accordance with an embodiment of the presentinvention; and

[0071]FIGS. 14 and 15 are graphs illustrating examples of a differencebetween a conventional channel estimator's performance and an inventivechannel estimator's performance in a predetermined wireless channelenvironment in accordance with embodiments of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0072] Embodiments of the present invention will be described in detailwith reference to the accompanying drawings. In the drawings, the sameor similar elements are denoted by the same reference numerals. In thefollowing description, a detailed description of known functions andconfigurations incorporated herein will be omitted for conciseness.

[0073] Prior to describing the embodiments of the present invention, thecharacteristics of a noise elimination filter will hereinafter bedescribed in detail.

[0074] The noise elimination filter determines its own characteristicsaccording to a filter coefficient composed of a feedback weight “a” andan input weight “b”. Provided that a DC gain is “1”, the feedback weight“a” and the input weight “b” have a predetermined relationship denotedby |b|=|1−a| in a primary IIR filter, and the feedback weight “a” andthe input weight “b” have a predetermined relationship denoted by${b} = {{1 - {\sum\limits_{k = 1}^{N}a_{k}}}}$

[0075] in an N-th IIR filter. In other words, the sum of the k=1feedback weight “a” and the input weight “b” is set to a predeterminednumber of 1. Therefore, if either one of the feedback weight “a” and theinput weight “b” is determined, then the other feedback weight isdetermined. For example, if the feedback weight “a” is set to “½”, thenthe input weight “b” is also set to “½”. As a result, the higher thefeedback weight “a”, the lower the input weight “b”, whereas the lowerthe feedback weight “a”, the higher the input weight “b”. The feedbackweight “a” and the input weight “b” are closely associated with thedegree of white noises, i.e., a SNR and a channel speed.

[0076] The relationship among filter coefficients “a” and “b”, the SNR,and the channel speed will hereinafter be described in detail.

[0077] The higher the feedback weight “a” (or the lower the input weight“b”), the higher the noise elimination efficiency of a noise eliminationfilter, because the feedback weight “a” smoothes a noise level,resulting in effectively removing randomly variable noises. However, thelower the feedback weight “a” (or the higher the input weight “b”), thelower the lagging phenomenon generated from the noise eliminationfilter, such that the noise elimination filter becomes suitable for ahigh-speed channel.

[0078] The method for predicting the degree of white noise contained ina pilot channel, the method for predicting the paging channel speed, andthe method for determining the best feedback weight “a” and the bestinput weight “b” based on the predicted information will now bedescribed in detail.

[0079] A. In the Case where Primary IIR filter is adapted as NoiseElimination Filter:

[0080] Firstly, it is assumed that a primary IIR filter for determiningits own characteristics based on one input weight “b” and one feedbackweight “a” is adapted as a noise elimination filter.

[0081] A1. One Example of Channel Estimator:

[0082] The channel estimator will hereinafter be described withreference to FIG. 5. FIG. 5 is a block diagram of an example of achannel estimator in accordance with an embodiment of the presentinvention. It should be noted that an antenna diversity (i.e.,Tx-diversity) is not considered in FIG. 5 for the convenience ofdescription and for a better understanding of the embodiments of thepresent invention. Although the Tx-diversity is considered in FIG. 5,FIG. 5 further needs only a circuit component for separating each signalfrom all signals received via many antennas, but this additional circuitcomponent is less associated with the fundamental principle of thepresent invention.

[0083] Referring to FIG. 5, input signals IN are provided to the firstintegration/dump filter 510. In an embodiment of the present invention,the input signals may be pilot signals received from common pilotchannels. The first integration/dump filter 510 accumulates the inputsignals (i.e., the pilot signals) in response to a SF (Spreading Factor)used for the common pilot channels, and numerically integrates theaccumulated input signals. A measurement detected by the firstintegration/dump filter 510 may be reception intensity in symbol unitsin association with the input signals. This measurement is provided to amultiplier 512.

[0084] A complex conjugate pattern generator 514 generates a complexconjugate pattern corresponding to a symbol pattern of a pilot signaltransmitted over the common pilot channel. The complex conjugate patterngenerated from the complex conjugate pattern generator 514 is providedto the multiplier 512. The multiplier 512 multiplies the complexconjugate pattern generated from the complex conjugate pattern generator514 by the measurement generated from the first integration/dump filter510, and thus generates a subdivided signal corresponding to a desiredantenna. The subdivided signal associated with the desired antenna isreceived from the multiplier 512, and is transmitted to the secondintegration/dump filter 516. The second integration/dump filter 516receives subdivided signals for desired antennas from the multiplier512, accumulates the subdivided signals in two-symbol units, numericallyintegrates the accumulated signals, and thus outputs a channelprediction value {tilde over (c)}(n). But, this channel prediction value{tilde over (c)}(n) does not consider the white noise contained in theinput signals IN. Therefore, the channel prediction value {tilde over(c)}(n) is transmitted to the noise elimination filter 522 to obtain amore accurate value considering the white noise.

[0085] The predicted paging channel response {tilde over (c)}(n)received from the integration/dump filter 516 is transmitted to achannel-speed/noise-level detector 518. The channel-speed/noise-leveldetector 518 detects a noise level α of a pilot channel upon receiving amean value of differences between the predicted fading channel response{tilde over (c)}(n) and a previously predicted fading channel response{tilde over (c)}(n−1). The channel-speed/noise-level detector 518calculates an auto-correlation function using the predicted fadingchannel response {tilde over (c)}(n), and measures a channel speedprediction parameter β according to either a minimum auto-correlationfunction for reflecting a changing rate of a channel or a mean value ofa plurality of auto-correlation functions. The sum of the noise level αof the pilot channel and the channel speed prediction parameter β is setto a predetermined number such as “1”. Thus, if either one of α and β ismeasured or detected, the other one can be easily determined without anadditional measurement process. A method for measuring the noise level αof the pilot channel and a method for measuring the channel speedprediction parameter β will hereinafter be described in detail.

[0086] The noise level α and the channel speed prediction parameter βmeasured by the channel-speed/noise-level detector 518 are provided to afilter coefficient controller 520. The filter coefficient controller 520compares the noise level α of the pilot channel and the channel speedprediction parameter β with predetermined mapping degrees shown in FIGS.8 and 10, and thus determines the feedback weight “a” and the inputweight “b”. In this case, the feedback weight “a” and the input weight“b” should be determined to implement an optimum IIR filter according toa current wireless channel environment. The filter coefficientcontroller 520 generates a control signal S for controlling operationsof a slope compensator 524 upon receiving a channel speed predictionparameter β. For example, if the channel speed prediction parameter β ishigher than a predetermined reference value T_(β), the filtercoefficient controller 520 controls the slope compensator 524 not to beoperated (i.e., S=0). If the channel speed prediction parameter 8 islower than the predetermined reference value T_(β), the filtercoefficient controller 520 controls the slope compensator 524 to beoperated (i.e., S=1). A method for determining the feedback weight “a”,the input weight “b”, and the slope compensation control signal S on thebasis of the noise level α and the channel speed prediction parameter βwill hereinafter be described in detail.

[0087] Although the channel-speed/noise-level detector 518 is separatefrom the filter coefficient controller 520 in FIG. 5, it should be notedthat the channel-speed/noise-level detector 518 and the filtercoefficient controller 520 may be integrated as one without departingfrom the scope of the present invention.

[0088] The noise elimination filter 522 receives the feedback weight “a”and the input weight “b”, and removes a white noise component from afading channel response {tilde over (c)}(n) predicted by theintegration/dump filter 516. The IIR filter may be adapted as the noiseelimination filter 522. A channel prediction value ĉ(n) having no whitenoise component is generated from the noise elimination filter 522, andis then transmitted to the slope compensator 524. The slope compensator524 compensates for a slope of the channel prediction value ĉ(n) uponreceiving a control signal S from the filter coefficient controller 520.For example, if the control signal S of “1” for determining a slopecompensation execution is transmitted from the filter coefficientcontroller 520 to the slope compensator 524, then the slope compensator524 compensates for the slope of the channel prediction value ĉ(n).Otherwise, if the control signal S of “0” for determining a slopecompensation execution is transmitted from the filter coefficientcontroller 520 to the slope compensator 524, then the slope compensator524 outputs the channel prediction value ĉ(n) as it is without anycompensation of the slope of the value ĉ(n). A detailed operation of theslope compensator 524 for compensating the slope of the channelprediction value ĉ(n) will hereinafter be described in detail.

[0089]FIG. 5 shows a block diagram of an apparatus for predictingoptimum filter coefficients “a” and “b” according to a variable wirelesschannel environment using the channel-speed/noise-level detector 518 andthe filter coefficient controller 520. According to an embodiment of thepresent invention, the noise elimination filter 522 can optimally removea white noise according to a current wireless channel environment.

[0090] A2. Method for measuring Noise Level α of Pilot Channel:

[0091] A method for measuring the noise level α of the pilot channelusing the fading channel response {tilde over (c)}(n) predicted by thechannel-speed/noise-level detector 518 will now be described in detail.

[0092] A complex pilot channel h(n) after despreading reception pilotchannels can be represented by the following Equation 7:

[0093] [Equation 7]

h(n)=A _(p) ·S _(p) ·c(n)+N(n)

[0094] where A_(p) is a magnitude (e.g., a signal amplitude) of a pilotchannel; s_(p) is 1+j, and serves as a pilot symbol; c(n) is a pagingchannel response that is accumulated and averaged using a spreadingfactor SF_(pilot) of the pilot channel; and N(n) is a white noise. Onthe other hand, “n” is set to one of 1 to M_(pilot), and the M_(pilot),is the number of pilot symbols for every packet.

[0095] The predicted fading channel response {tilde over (c)}(n) appliedto an input terminal of the channel-speed/noise-level detector 518 isderived by multiplying a complex conjugate s·_(p) of a correspondingpilot symbol by the complex pilot channel h(n). The predicted fadingchannel response {tilde over (c)}(n) can be represented by the followingEquation 8: $\begin{matrix}\begin{matrix}{{c(n)} = {{h(n)} \cdot \frac{s_{p}^{*}}{2}}} \\{= {{A_{p} \cdot {c(n)} \cdot s_{p} \cdot \frac{s_{p}^{*}}{2}} + {{N(n)} \cdot \frac{s_{p}^{*}}{2}}}} \\{= {{A_{p} \cdot {c(n)}} + {N_{1}(n)}}}\end{matrix} & \text{[Equation~~8]}\end{matrix}$

[0096] Because a real fading channel c(n) is configured in the form of acontinuously changing sinusoidal wave (Sin), a nearby value such as adifference Δ=c(n)−c(n−1) between c(n) and c(n−1) is determineddifferently by a channel speed as shown in FIGS. 6A and 6B, such thatthe nearby value is approximately close to “0”. Specifically, FIG. 6a isa graph illustrating an example of a difference between a plurality ofvalues adjacent to each other in a high-speed channel when a generalchannel estimation is performed in accordance with an embodiment of thepresent invention. FIG. 6b is a graph illustrating an example of adifference between a plurality of values adjacent to each other in alow-speed channel when a general channel estimation is performed inaccordance with an embodiment of the present invention. Therefore, amean value a of differences between the predicted fading channelresponse {tilde over (c)}(n) and the previously predicted fading channelresponse {tilde over (c)}(n−1) can be calculated by the followingEquation 9: $\begin{matrix}\begin{matrix}{\alpha = {\frac{1}{M_{pilot}}{\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)} - {\overset{\sim}{c}\left( {n - 1} \right)}}}}}} \\{= {\frac{1}{M_{pilot}}{\sum\limits_{n}{{{A_{p} \cdot {c(n)}} + {N_{1}(n)} - {A_{p} \cdot {c\left( {n - 1} \right)}} - {N_{1}\left( {n - 1} \right)}}}}}} \\{\approx {\frac{1}{M_{pilot}}{\sum\limits_{n}{{{N_{1}(n)} - {N_{1}\left( {n - 1} \right)}}}}}}\end{matrix} & \text{[Equation~~9]}\end{matrix}$

[0097] Referring to the Equation 9, a fading channel component A_(p)derived by multiplication of {tilde over (c)}(n) and {tilde over(c)}(n−1) is eliminated to leave only noise components in such a waythat a noise level of the pilot channel is recognized.

[0098] As shown in the Equation 9, the difference between the predictedfading channel response {tilde over (c)}(n) and the previously predictedfading channel response {tilde over (c)}(n−1) delayed by one symbol isclose to “0”, such that only a noise component |N₁(n)−N₁(n−1)| iscalculated while the fading channel component A_(p) is eliminated.Provided that a mean value of noise components calculated by all thepredicted fading channel responses {tilde over (c)}(n) is calculated, anoise level α of the pilot channel can be approximately measured.Because the mean value of the noise components is proportional to N(n)as denoted by α·N(n), a mean value of the noise components can beadapted as an index for indicating a noise level α of the pilot channel.

[0099] A3. Method for measuring Channel Speed Prediction Parameter β:

[0100] A method for measuring a channel speed prediction parameter βusing a fading channel response {tilde over (c)}(n) predicted by thechannel-speed/noise-level detector 518 shown in FIG. 5 will hereinafterbe described in detail.

[0101] An auto-correlation function R_({tilde over (e)}(n)) (l) using anoutput signal {tilde over (c)}(n)=A_(p)·c(n)+N₁(n) of theintegration/dump filter 516 can be calculated by the following Equation10: $\begin{matrix}{{R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}} & \text{[Equation~~10]}\end{matrix}$

[0102] As shown in the Equation 9, a minimum value or a mean value ofauto-correlation functions calculated by the Equation 10 reflects avariation of channel speeds therein. Therefore, the channel speedprediction parameter β can be represented by the following Equation 11:$\begin{matrix}{{\beta = {{\min \left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}\quad \text{or}}}\quad {\beta = {{mean}\left\lbrack {{{R_{\overset{\sim}{c}}(l)}/\max}\quad \left( R_{\overset{\sim}{c}} \right)} \right\rbrack}}} & \text{[Equation~~11]}\end{matrix}$

[0103] where β satisfies a predetermined condition 0≦β≦1. The channelspeed prediction parameter β is provided in the form of a normalizationof the auto-correlation function, and thus sufficiently designates avariation of channel conditions. Specifically, when using a slow fadingmode during which there is little variation of channel environments, thechannel speed prediction parameter β is approximately close to “1”. Whenusing a fast fading mode during which a channel condition or environmentis abruptly changed, the parameter β is approximately close to “0”.

[0104] A4. Method for determining Feedback Weight “a” and Input Weight“b”:

[0105] A noise level α and a channel speed prediction parameter β of thepilot channel can be determined by the filter coefficient controller 520shown in FIG. 5, and this method for determining the noise level α andthe channel speed prediction parameter β will now be described indetail. The feedback weight “a” and the input weight “b” aredistinguished from each other according to a determination method basedon a single reference value T_(α) or T_(β) and other determinationmethod based on multi-level reference values T_(α1), T_(α2), T_(β1), andT_(β2).

[0106] A method for determining the feedback weight “a” and the inputweight “b” on the basis of a single-level reference value willhereinafter be described with reference to FIGS. 7a, 7 b, and 8.

[0107]FIG. 7a is a graph illustrating an example of a SNR associatedwith a single-level reference value and a SNR associated with the amountof noise of a detected pilot channel in accordance with an embodiment ofthe present invention. FIG. 7b is a graph illustrating an example of achannel speed associated with a single-level reference value and achannel speed associated with a detected channel speed predictionparameter in accordance with an embodiment of the present invention.Specifically, FIG. 7a is an example of a real mapping-processed SNR(dB)in response to a single-level reference value and a noise level α of ameasured pilot channel. FIG. 7b is an example of a realmapping-processed channel speed in response to a single-level referencevalue and a channel speed prediction parameter β.

[0108]FIG. 8 is a graph illustrating an example of a mapping example offilter coefficients corresponding to a plurality of measurements when asingle-level reference value is adapted to the present invention inaccordance with an embodiment of the present invention. Specifically,FIG. 8 is an example where the feedback weight “a”, the input weight“b”, and a slope control signal “S” are optimally mapping-processedaccording to the noise level α of a measured pilot channel and thechannel speed prediction parameter β when using single-level referencevalues T_(α) and T_(β).

[0109] Upon receiving a noise level α from the channel-speed/noise-leveldetector 518, the filter coefficient controller 520 determines a SNR(dB)corresponding to α on the basis of the graph shown in FIG. 7a. Thefilter coefficient controller 520 compares the determined SNR with aSNR(dB) of the reference value T_(α). Upon receiving the channel speedprediction parameter β from the channel-speed/noise-level detector 518,the filter coefficient controller 520 determines a channel speedcorresponding to β on the basis of the graph shown in FIG. 7b. In thiscase, the reference values T_(α) and T_(β) function as a conversionpoint for converting mapping rules of the filter coefficients “a” and“b” into another mapping rule, and may be differently adapted accordingto a hardware structure and performance of individual receivers. Thestructure and performance of the receivers are determined differentlyaccording to a system designer or a system standard. The SNR value (dB)corresponding to T_(α) and the channel speed (km) corresponding to T_(β)are calculated by a receiver's performance test. That is, the SNR(dB)corresponding to T_(α) and the channel speed corresponding to T_(β) aredetermined using many tests performed by users.

[0110] Upon receiving the comparing results associated with SNRs andchannel speeds, the filter coefficient controller 520 determines thefeedback weight “a”, the input weight “b” and a control signal S on thebasis of mapping degrees shown in FIG. 8. The control signal S isadapted to determine whether a slope is compensated or not. Four mappingrules in response to the above comparing results are shown in FIG. 8illustrating various mapping degrees. Referring to FIG. 8, apredetermined area of more than 5 dB is adapted as a low-noise area inthe case of SNR, and a predetermined channel of more than 500 km isadapted as a high-speed channel.

[0111] Firstly, if a prescribed condition of β>T_(β) and α<T_(α) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ¾, and sets the input weight “b” to ¼, as shown in the mappingdegrees of FIG. 8. Further, a slope control signal S is set to “0” fordisabling a slope compensation function.

[0112] Secondly, if a prescribed condition of β>T_(β) and α>T_(α) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ⅞, and sets the input weight “b” to ⅛, as shown in the mappingdegrees of FIG. 8. Further, a slope control signal S is also set to “0”for disabling a slope compensation function.

[0113] Thirdly, if a prescribed condition of β<T_(β) and α<T_(α) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ¼, and sets the input weight “b” to ¾, as shown in the mappingdegrees of FIG. 8. Further, a slope control signal S is set to “1” foractivating a slope compensation function.

[0114] Fourth, if a prescribed condition of β<T_(β) and α>T_(α) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ½, and sets the input weight “b” to ½, as shown in the mappingdegrees of FIG. 8. Further, a slope control signal S is set to “1” foractivating a slope compensation function.

[0115] A method for determining the feedback weight “a” and the inputweight “b” on the basis of multi-level reference values will hereinafterbe described with reference to FIGS. 9a, 9 b, and 10.

[0116]FIG. 9a is a graph illustrating an example of a SNR associatedwith a multi-level reference value and a SNR associated with the amountof noise of a detected pilot channel in accordance with an embodiment ofthe present invention. FIG. 9b is a graph illustrating an example of achannel speed associated with a multi-level reference value and achannel speed associated with a detected channel speed predictionparameter in accordance with an embodiment of the present invention.FIG. 10 is a graph illustrating an example of a mapping example offilter coefficients corresponding to a plurality of measurements whenmulti-level reference values are adapted to the present invention inaccordance with an embodiment of the present invention. FIG. 9a is anexample of a real mapping-processed SNR(dB) in response to multi-levelreference values and a noise level α of a measured pilot channel. FIG.9b is an example of a real mapping-processed channel speed in responseto multi-level reference values and a channel speed prediction parameterβ. FIG. 10 is an example where the feedback weight “a”, the input weight“b”, and a slope control signal “S” are optimally mapping-processedaccording to the noise level α of a measured pilot channel and thechannel speed prediction parameter β in the case of using multi-levelreference values T_(α1), T_(α2), T_(β1), and T_(β2). In this case, FIG.9a shows three areas on the basis of specific values of 0 dB (T_(α2))and 10 dB (T_(α1)), i.e., a low-noise area, an intermediate-noise area,and a high-noise area. In the case of a channel speed, FIG. 9b showsthree areas on the basis of specific values of 120 km (T_(β2)) and 10 dB(T_(β1)), i.e., a low-speed channel area, an intermediate-speed channelarea, and a high-noise channel area.

[0117] Upon receiving an output signal α from thechannel-speed/noise-level detector 518, the filter coefficientcontroller 520 compares a SNR(dB) value corresponding to a noise level αof a pilot channel with individual SNR(dB) values corresponding to thereference values T_(α1) and T_(α2). Upon receiving an output signal βfrom the channel-speed/noise-level detector 518, the filter coefficientcontroller 520 compares a channel speed corresponding to the channelspeed prediction parameter β with individual channel speedscorresponding to the reference values T_(β1) and T_(β2).

[0118] Upon receiving the comparing results associated with SNRs andchannel speeds, the filter coefficient controller 520 determines thefeedback weight “a”, the input weight “b” and a control signal S on thebasis of mapping degrees shown in FIG. 10. In this case, the controlsignal S is adapted to determine whether a slope is compensated or not.Nine mapping rules in response to the above comparing results are shownin FIG. 10 illustrating various mapping degrees.

[0119] Firstly, if a prescribed condition of β>T_(β2) and α<T_(α1) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ¾, and sets the input weight “b” to ¼, as shown in the mappingdegrees of FIG. 10. Further, a slope control signal S is set to “0” fordisabling a slope compensation function.

[0120] Secondly, if a prescribed condition of β>T_(β2) andT_(α1)<α<T_(α2) is provided, the filter coefficient controller 520 setsthe feedback weight “a” to ⅞, and sets the input weight “b” to ⅛, asshown in the mapping degrees of FIG. 10. Further, a slope control signalS is also set to “0” for disabling a slope compensation function.

[0121] Thirdly, if a prescribed condition of β>T_(β2) and α>T_(α2) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to {fraction (15/16)}, and sets the input weight “b” to {fraction(1/16)}, as shown in the mapping degrees of FIG. 10. Further, a slopecontrol signal S is also set to “0” for disabling a slope compensationfunction.

[0122] Fourth, if a prescribed condition of T_(β2)<β<T_(α1) and α<T_(α1)is provided, the filter coefficient controller 520 sets the feedbackweight “a” to ½, and sets the input weight “b” to ½, as shown in themapping degrees of FIG. 10. Further, a slope control signal S is set to“1” for activating a slope compensation function.

[0123] Fifth, if a prescribed condition of T_(β2)<β<T_(β1) andT_(α1)<α<T_(α2) is provided, the filter coefficient controller 520 setsthe feedback weight “a” to ¾, and sets the input weight “b” to ¼, asshown in the mapping degrees of FIG. 10. Further, a slope control signalS is set to “1” for activating a slope compensation function.

[0124] Sixth, if a prescribed condition of T_(β2)<β<T_(β1) and α>T_(α2)is provided, the filter coefficient controller 520 sets the feedbackweight “a” to ⅞, and sets the input weight “b” to ⅛, as shown in themapping degrees of FIG. 10. Further, a slope control signal S is set to“1” for activating a slope compensation function.

[0125] Seventh, if a prescribed condition of β<T_(β1) and α<T_(α1) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ¼, and sets the input weight “b” to ¾, as shown in the mappingdegrees of FIG. 10. Further, a slope control signal S is set to “1” foractivating a slope compensation function.

[0126] Eighth, if a prescribed condition of β<T_(β1) and T_(α1)<α<T_(α2)is provided, the filter coefficient controller 520 sets the feedbackweight “a” to ½, and sets the input weight “b” to ½, as shown in themapping degrees of FIG. 10. Further, a slope control signal S is set to“1” for activating a slope compensation function.

[0127] Ninth, if a prescribed condition of β<T_(β1) and α>T_(α2) isprovided, the filter coefficient controller 520 sets the feedback weight“a” to ¾, and sets the input weight “b” to ¼, as shown in the mappingdegrees of FIG. 10. Further, a slope control signal S is set to “1” foractivating a slope compensation function.

[0128] The above mapping examples shown in FIGS. 8 and 10 define filtercoefficients for enabling a noise elimination filter 522 to perform anoptimum function. In an embodiment of the present invention, the optimumfilter coefficients can be implemented using a receiver's performancetest.

[0129] A5. Slope Compensation Method

[0130] A method for controlling a slope compensator 524 to perform aslope compensation according to a channel speed prediction parameter βwill now be described in detail.

[0131] It should be noted that the following slope compensation methodis controlled by the filter coefficient controller 520 according to achannel speed. The slope compensation is determined by the channel speedbecause the slope compensation effect is shown differently according tothe channel speed. Specifically, the slope compensation effect in eithera high-speed channel or a wireless channel environment of a high SNR isrelatively high, whereas the slope compensation effect in either alow-speed channel or a wireless channel environment of a low SNR isrelatively low, because a ratio of channel variation rate Δ=c(n)−c(n−1)to a noise N(n) is relatively high in the high-speed channel. Thus, aprobability of creating a channel compensation value Δ(n) is relativelyhigh. Therefore, it is preferable for the slope compensation to beperformed when the ratio Δ/N(n) of channel variation rate to noise issufficiently provided.

[0132] When using the single-level reference values T_(α) and T_(β)shown in FIG. 8, if a predetermined condition of β>T_(β) is provided, Sis set to “0”, such that the slope compensation is not performed.Otherwise, if a predetermined condition of β≦T_(β) is provided, S is setto “1”, such that the slope compensation is performed.

[0133] When using the multi-level reference values T_(α1), T_(α2),T_(β1) and T_(β2) shown in FIG. 10, if a predetermined condition ofβ>T_(β2) is provided, S is set to “0”, such that the slope compensationis not performed. Otherwise, if a predetermined condition of β≦T_(β2) isprovided, S is set to “1”, such that the slope compensation isperformed.

[0134] However, although T_(β) is adapted as a reference value forsetting S to “0” or “1”, an additional reference value other than T_(β)may be selectively adapted if needed. In order to determine the signal Sdifferently from the reference values T_(α) and T_(β) for determiningfilter coefficients, FIG. 11 shows an example where reference valuesT_(αs) and T_(βt) are adapted independently from each other.Specifically, FIG. 11 is a graph illustrating an example where anindependent reference value is adapted to determine whether a slope iscompensated or not in accordance with an embodiment of the presentinvention

[0135] The slope compensator 524 is connected to the last end of achannel estimator in order to adjust filter coefficients based on α andβ while improving another performance. The principle of the slopecompensation can be recognized by a difference between a pilot channelSF SF_(date) and a data channel SF SF_(pilot). Typically, the SF_(pilot)is considerably higher than the SF_(date). For example, if theSF_(pilot) is 256, the SF_(date) is 16. In this case, the number of datasymbols for every symbol is 16. Therefore, a channel compensation valuepredicted by one pilot symbol is equally applied to 16 data symbols.Provided that a Tx-diversity also called an antenna diversity isconsidered, 32 data symbols are channel-compensated by a channelcompensation value predicted by one pilot symbol. This fact is clearlyshown in FIG. 12 which is a graph illustrating an example of theprinciple of slope compensation in accordance with an embodiment of thepresent invention.

[0136] Therefore, it is preferable for the channel estimator to predicta slope status of a current channel and respectively apply differentchannel compensation values to 16 data symbols. Different channelcompensation values Δ(n) determined by individual data symbols can berepresented by the following Equation 12: $\begin{matrix}{{\Lambda (n)} = {\frac{1}{W \cdot \left( {{SF}_{pilot}/{SF}_{data}} \right)}{\sum\limits_{w = 0}^{w - 1}{{{\hat{c}\left( {n - w} \right)} - {\hat{c}\left( {n - w - 1} \right)}}}}}} & \text{[Equation~~12]}\end{matrix}$

[0137] where n is an index of a pilot symbol contained in one packet,and w is a size of window used for slope prediction.

[0138] If the channel compensation value Δ(n) calculated by the Equation12 is applied to the signal ĉ(n) having no noise, an output signal ofthe slope compensator 524 can be calculated by the following Equation13:

[0139] [Equation 13]

c _(est)(n,k)={circumflex over (c)}(n)·k·Δ(n)

[0140] where c_(est)(n, k) is a channel-compensated output signal ofk-th data symbol with respect to an n-th pilot symbol, k is1≦k≦SF_(pilot)/SF_(date), and SF_(pilot)/SF_(date) is the number of datasymbols associated with one pilot symbol. Therefore, the number of datasymbols for every packet can be denoted byM_(pilot)×SF_(pilot)/SF_(date).

[0141] A6. Operations of Preferred Embodiment of the Present Invention

[0142] Operations of a channel estimator in accordance with a preferredembodiment of the present invention will hereinafter be described withreference to FIG. 13 which is a flow chart illustrating an example ofoperations of the channel estimator in accordance with an embodiment ofthe present invention.

[0143] Referring to FIG. 13, a channel estimator receives apredetermined channel signal from a transmitter at step 1300. In thiscase, the predetermined channel signal is a channel signal forrecognizing information of a wireless channel environment, and, in anembodiment of the present invention, may be a pilot signal received overa common pilot channel. The above step 1300 includes a variety ofoperations executed in the first integration/dump filter 510, themultiplier 512, and the second integration/dump filter 516. Uponreceiving the predetermined channel signal at step 1300, then thechannel estimator goes to step 1302 to measure a noise level α of apilot channel and a channel speed prediction parameter β, that areassociated with the predetermined channel signal, using thechannel-speed/noise-level detector 518. If the noise level α of thepilot channel and the channel speed prediction parameter β is detectedat step 1302, the channel estimator goes to step 1304. The filtercoefficient controller 520 of the channel estimator provides the noiselevel α of the pilot channel and the channel speed prediction parameterβ to a predetermined mapping degree at step 1304, and thus determinesfilter coefficient “a” and “b” for optimally coping with a currentwireless channel environment and a control signal S for determiningwhether a slope is compensated or not. The filter coefficient isinformation required by the noise elimination filter 522, and includesthe feedback weight “a” and the input weight “b”. The determined filtercoefficients “a” and “b” is provided to the noise elimination filter522, and the control signal is provided to the slope compensator 524. Anoise component may be contained in the predetermined channel signal.Upon receiving filter coefficients “a” and “b” from the filtercoefficient controller 520, the noise elimination filter 522 removes thenoise component from the predetermined channel signal using the receivedfilter coefficients “a” and “b” at step 1306. The slope compensator 524determines whether a slope compensation is adapted to the predeterminedchannel signal having no noise at step 1308. Specifically, if S=1, theslope compensator 524 determines that slope compensation is requested.Otherwise, if S=0, the slope compensator 524 determines that slopecompensation is not requested. The slope compensator 524 goes to step1310 upon receiving the slope compensation request signal, and performsslope compensation of the predetermined channel signal having no noiseat step 1310. Otherwise, if slope compensation is not requested at step1308, the channel estimator terminates a control program withoutexecuting the step 1310.

[0144] B. In the Case Where N-th IIR Filter is Adapted as NoiseElimination Filter:

[0145] The filter coefficients “a” and “b” determined by theconfiguration and operations of the aforementioned embodiment of thepresent invention may be equally applied to the primary noiseelimination filter and the N-th noise elimination filter. However, thefilter coefficients “a” is implemented differently according to thecharacteristics of the N-th noise elimination filter. Specifically, whenusing the N-th noise elimination filter, different filter coefficientsa₁ to a_(n) are respectively assigned to N noise elimination filterssuch as a filter coefficient “a”. This distribution method may beclassified into an equal distribution method and an unequal distributionmethod.

[0146] In the case of the equal distribution method, a result valuederived by division between “a” and the number N of feedback signals isadapted as feedback weights a₁ to a_(n) associated with individualfeedback signals. In other words, the weights a₁ to a_(N) associatedwith individual feedback signals can be defined as a₁=a₂= . . .a_(N)=a/N.

[0147] The unequal distribution method controls the coefficient “a” tosatisfy a predetermined condition of W₁a₁+W₂ a₂+ . . . +W_(N)a_(N)=awhen distributing the determined value “a” to individual feedbackweights a₁ to a_(N). In this case, although W₁ to W_(N) are assigneddifferent weights, they must preferably satisfy a prescribed conditionof $\sum\limits_{k = 1}^{N}{W_{k}.}$

[0148] Each of W₁ to W_(N) can be adjusted when designing a noiseelimination filter, they must preferably satisfy a predeterminedcondition of W₁>W₂> . . . >W_(N).

[0149] C. Performance Comparison

[0150]FIG. 14 is a graph illustrating an example of a difference betweena conventional channel estimator's performance and an inventive channelestimator's performance in a low-speed wireless channel environment inaccordance with an embodiment of the present invention. FIG. 15 is agraph illustrating an example of a difference between a conventionalchannel estimator's performance and an inventive channel estimator'sperformance in a high-speed wireless channel environment in accordancewith an embodiment of the present invention. The 16QAM is adapted as amodulation method in FIG. 14, and a flat fading having no fadingvariance can be applied to FIG. 14. In FIG. 14, it is assumed that achannel speed of 3 km is adapted as a flat fading condition. The 16QAMis also adapted as a modulation method in FIG. 15, and it is assumedthat a channel speed of 120 km is adapted as such flat fading conditionin FIG. 15. On the other hand, FIGS. 14 and 15 commonly adapt asingle-level reference value shown in FIG. 8. In this case, prescribedvalues, i.e., T_(β)=0.3 and T_(α)=0.2, are adapted as such referencevalues, or other prescribed values. i.e., T_(≢s)=0 and T_(βt)=T_(β), areadapted as such reference values.

[0151] As shown in FIG. 14, the channel estimator according toembodiments of the present invention is superior to the conventionalchannel estimator in a low-speed channel. Further, a channel estimatorhaving an adaptive noise elimination filter described in the embodimentsof the present invention has a gain higher than that of the conventionalchannel estimator.

[0152]FIG. 15 shows the lagging phenomenon created in either the case ofadapting a noise elimination filter in a high-speed channel, or the caseof not using the noise elimination filter in the high-speed channel.Although in an embodiment of the present invention, it is preferable forthe noise elimination filter not to be used for the high-speed channel,it is impossible for the noise elimination filter to be used for only aspecific wireless channel environment. Therefore, a noise eliminationfilter is controlled according to information of a wireless channelenvironment, such that an additional gain can be obtained in not only alow-speed channel but also a high-speed channel.

[0153] As apparent from the above description, according to embodimentsof the present invention, an inventive channel estimator having beeneffectively used for only a low-order modulation scheme and a low coderate can be used in a high-order modulation scheme and a high code rate,resulting in a high-speed packet transmission. Each filter coefficientof a noise elimination filter is adjusted to be suitable for individualwireless channel characteristics, and is thus available for a variety ofwireless channel environments, resulting in increasing the efficiency ofthe channel estimator.

[0154] Although the embodiments of the present invention have beendisclosed for illustrative purposes, those skilled in the art willappreciate that various modifications, additions and substitutions arepossible, without departing from the scope and spirit of the inventionas disclosed in the accompanying claims.

What is claimed is:
 1. A method for determining first and second filtercoefficients in a noise elimination filter which receives a predictedchannel response signal and the first and second filter coefficientswhere their sum is set to a predetermined value, and removes a noisecomponent from the predicted channel response signal, comprising thesteps of: a) detecting a noise level upon receiving a difference betweenthe predicted channel response signal and a previously predicted channelresponse signal, and detecting channel speed prediction information uponreceiving an auto-correlation function of the predicted channel responsesignal; and b) determining first and second filter coefficientsmapping-processed by the detected noise level and the detected channelspeed prediction information.
 2. The method as set forth in claim 1,further comprising the step of: c) if the detected channel speedprediction information value is lower than a predetermined thresholdvalue, performing a slope compensation of a channel response signal ofwhich the noise component is eliminated by the first and second filtercoefficients.
 3. The method as set forth in claim 1, wherein the noiselevel α is calculated by the following equation:$\alpha = {\frac{1}{M_{pilot}}{\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)} - {\overset{\sim}{c}\left( {n - 1} \right)}}}}}$

where M_(pilot) is the number of pilot symbols for every packet, {tildeover (c)}(n) is the predicted channel response signal, and {tilde over(c)}(n−1) is the previously predicted channel response signal.
 4. Themethod as set forth in claim 1, wherein the channel speed predictioninformation is obtained by the following equations:$\beta = {\min \left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and min[x] is aminimum value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where R_({tilde over (c)}(n)) is an auto-correlation function using thepredicted channel response signal {tilde over (c)}(n).
 5. The method asset forth in claim 1, wherein the channel speed prediction informationis obtained by the following equations:$\beta = {{mean}\left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and mean[x] is amean value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 6. The method as set forth in claim 2,wherein the step (c) for performing the slope compensation includes thesteps of: c1) respectively assigning different channel compensationvalues Δ(n) calculated by the following first equation to a plurality ofdata symbols corresponding to one pilot symbol; and c2) adapting theassigned channel compensation values Δ(n) to the following secondequation, and performing slope compensation operations for every datasymbol, wherein said first equation is represented by:${\Lambda (n)} = {\frac{1}{W \cdot \left( {{SF}_{pilot}/{SF}_{data}} \right)}{\sum\limits_{w = 0}^{w - 1}{{{\hat{c}\left( {n - w} \right)} - {\hat{c}\left( {n - w - 1} \right)}}}}}$

where n is an index of a pilot symbol contained in one packet, w is asize of window used for slope prediction, ĉ(n) is a signal of which anN-th noise is eliminated, SF_(pilot) is a spreading factor (SF) of apilot channel, SF_(date) is a spreading factor (SF) of a data channel,and SF_(pilot)/SF_(date) is the number of data symbols corresponding toone pilot symbol, wherein said second equation is represented by: c_(est)(n,k)={circumflex over (c)}(n)·k·Δ(n) (1≦k≦SF _(pilot) /SF_(date)) where c_(est)(n,k) is a channel-compensated output signal ofk-th data symbol with respect to an N-th pilot symbol.
 7. The method asset forth in claim 1, wherein the second filter coefficient is equallydistributed to N coefficients when the noise elimination filter is anN-th noise elimination filter.
 8. The method as set forth in claim 1,wherein the second filter coefficient is unequally distributed to Ncoefficients due to different weights, when the noise elimination filteris an N-th noise elimination filter.
 9. The method as set forth in claim1, wherein the first and second filter coefficients are determined to betwo filter coefficients of one group selected from among a plurality offilter coefficient groups, each group being composed of two filtercoefficients, mapping-processed for every area differently assignedaccording to at least one first reference value for discriminatingbetween noise levels and at least one second reference value fordiscriminating between channel speed prediction information, saidselected one group being selectively determined according to thedetected noise level and the detected channel speed predictioninformation.
 10. An apparatus for determining first and second filtercoefficients in a noise elimination filter which receives a predictedchannel response signal and the first and second filter coefficientswhere their sum is set to a predetermined value, and removes a noisecomponent from the predicted channel response signal, comprising: achannel-speed/noise-level detector for detecting a noise level uponreceiving a difference between the predicted channel response signal anda previously predicted channel response signal, and detecting channelspeed prediction information upon receiving an auto-correlation functionof the predicted channel response signal; and a filter coefficientcontroller for determining first and second filter coefficientsmapping-processed by the detected noise level and the detected channelspeed prediction information.
 11. The apparatus as set forth in claim10, wherein the filter coefficient controller, if the detected channelspeed prediction information value is lower than a predeterminedthreshold value, controls a slope compensator to perform a slopecompensation of a channel response signal of which a noise component iseliminated by the first and second filter coefficients.
 12. Theapparatus as set forth in claim 10, wherein the noise level α iscalculated by the following equation:$\alpha = {\frac{1}{M_{pilot}}{\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)} - {\overset{\sim}{c}\left( {n - 1} \right)}}}}}$

where M_(pilot) is the number of pilot symbols for every packet, {tildeover (c)}(n) is the predicted channel response signal, and {tilde over(c)}(n−1) is the previously predicted channel response signal.
 13. Theapparatus as set forth in claim 10, wherein the channel speed predictioninformation is obtained by the following equations:$\beta = {\min \left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and min[x] is aminimum value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 14. The apparatus as set forth in claim 10,wherein the channel speed prediction information is obtained by thefollowing equations:$\beta = {{mean}\left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and mean[x] is amean value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 15. The apparatus as set forth in claim 11,wherein the slope compensator respectively assigns different channelcompensation values Δ(n) calculated by the following first equation to aplurality of data symbols corresponding to one pilot symbol, and adaptsthe assigned channel compensation values Δ(n) to the following secondequation, and performing slope compensation operations for every datasymbol, wherein said first equation is represented by:${\Lambda (n)} = {\frac{1}{W \cdot \left( {{SF}_{pilot}/{SF}_{data}} \right)}{\sum\limits_{w = 0}^{w - 1}{{{\hat{c}\left( {n - w} \right)} - {\hat{c}\left( {n - w - 1} \right)}}}}}$

where n is an index of a pilot symbol contained in one packet, w is asize of window used for slope prediction, ĉ(n) is a signal of which anN-th noise is eliminated, SF_(pilot) is a spreading factor (SF) of apilot channel, SF_(date) is a spreading factor (SF) of a data channel,and SF_(pilot)/SF_(date) is the number of data symbols corresponding toone pilot symbol, wherein said second equation is represented by: C_(est)(n,k)=ĉ(n)·k·Δ(n) (1≦k≦SF _(pilot) /SF _(date)) where c_(est)(n,k) is a channel-compensated output signal of k-th data symbol withrespect to an N-th pilot symbol.
 16. The apparatus as set forth in claim10, wherein the filter coefficient controller equally distributes thesecond filter coefficient to N coefficients when the noise eliminationfilter is an N-th noise elimination filter.
 17. The apparatus as setforth in claim 10, wherein the filter coefficient controller unequallydistributes the second filter coefficient to N coefficients due todifferent weights, when the noise elimination filter is an N-th noiseelimination filter.
 18. The apparatus as set forth in claim 10, whereinthe filter coefficient controller determines first and second filtercoefficients to be two filter coefficients of one group among aplurality of filter coefficient groups, each group being composed of twofilter coefficients, mapping-processed for every area differentlyassigned according to at least one first reference value fordiscriminating between noise levels and at least one second referencevalue for discriminating between channel speed prediction information,said selected one group being selectively determined according to thedetected noise level and the detected channel speed predictioninformation.
 19. A method for receiving a common pilot channel signal atan adaptive channel estimator of a mobile communication system, andremoving a noise from the received common pilot channel signal,comprising the steps of: a) multiplying a complex conjugate of acorresponding pilot symbol by the common pilot channel signal, andoutputting a predicted fading channel response signal; b) detecting anoise level contained in the predicted fading channel response signal;c) detecting a channel speed of the common pilot channel signal on thebasis of the predicted fading channel response; d) comparing thedetected noise level with at least one first reference value; e)comparing the detected channel speed with at least one second referencevalue; f) determining first and second filter coefficients mapped to anarea corresponding to the comparing result to be filter coefficients fornoise elimination, said first and second filter coefficients beingmapping-processed for every area assigned by the first and secondreference values; and g) removing a noise component from the predictedfading channel response signal using the determined first filtercoefficient and the determined second coefficient.
 20. The method as setforth in claim 19, further comprising the step of: h) if the detectedchannel speed prediction information value is lower than a predeterminedthreshold value, performing a slope compensation of a channel responsesignal of which a noise component is eliminated by the first and secondfilter coefficients.
 21. The method as set forth in claim 19, whereinthe noise level α is calculated by the following equation:$\alpha = {\frac{1}{M_{pilot}}{\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)} - {\overset{\sim}{c}\left( {n - 1} \right)}}}}}$

where M_(pilot) is the number of pilot symbols for every packet, {tildeover (c)}(n) is the predicted channel response signal, and {tilde over(c)}(n−1) is the previously predicted channel response signal.
 22. Themethod as set forth in claim 19, wherein the channel speed predictioninformation is obtained by the following equations:$\beta = {\min \left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and min[x] is aminimum value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 23. The method as set forth in claim 19,wherein the channel speed prediction information is obtained by thefollowing equations:$\beta = {{mean}\left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and mean[x] is amean value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where R_({tilde over (e)}(n))(l) is an auto-correlation function usingthe predicted channel response signal {tilde over (c)}(n).
 24. Themethod as set forth in claim 20, wherein the step (h) for performing theslope compensation includes the steps of: h1) respectively assigningdifferent channel compensation values Δ(n) calculated by the followingfirst equation to a plurality of data symbols corresponding to one pilotsymbol; and h2) adapting the assigned channel compensation values Δ(n)to the following second equation, and performing slope compensationoperations for every data symbol, wherein said first equation isrepresented by:${\Lambda (n)} = {\frac{1}{W \cdot \left( {{SF}_{pilot}/{SF}_{data}} \right)}{\sum\limits_{w = 0}^{w - 1}{{{\hat{c}\left( {n - w} \right)} - {\hat{c}\left( {n - w - 1} \right)}}}}}$

where n is an index of a pilot symbol contained in one packet, w is asize of window used for slope prediction, ĉ(n) is a signal of which anN-th noise is eliminated, SF_(pilot) is a spreading factor (SF) of apilot channel, SF_(date) is a spreading factor (SF) of a data channel,and SF_(pilot)/SF_(date) is the number of data symbols corresponding toone pilot symbol, wherein said second equation is represented by: c_(est)(n,k)={circumflex over (c)}(n)·k··(n) (1≦k≦SF _(pilot) /SF_(date)) where c_(est)(n, k) is a channel-compensated output signal ofk-th data symbol with respect to an N-th pilot symbol.
 25. The method asset forth in claim 19, wherein the second filter coefficient is equallydistributed to N coefficients when the noise elimination filter is anN-th noise elimination filter.
 26. The method as set forth in claim 19,wherein the second filter coefficient is unequally distributed to Ncoefficients due to different weights, when the noise elimination filteris an N-th noise elimination filter.
 27. An apparatus for receiving acommon pilot channel signal at an adaptive channel estimator of a mobilecommunication system, and removing a noise from the received commonpilot channel signal, comprising: a multiplier and an integration/dumpfilter for multiplying a complex conjugate of a corresponding pilotsymbol by the common pilot channel signal, and outputting a predictedfading channel response signal; a channel-speed/noise-level detector fordetecting a noise level contained in the predicted fading channelresponse signal, and detecting a channel speed of the common pilotchannel signal; a filter coefficient controller for setting first andsecond filter coefficients mapped with an area corresponding to thecomparing result to noise elimination filter coefficients, said firstand second filter coefficients being mapping-processed for every areaassigned by the first and second reference values; and a noiseelimination filter for removing a noise component from the predictedfading channel response signal using the determined first filtercoefficient and the determined second coefficient.
 28. The apparatus asset forth in claim 27, wherein the filter coefficient controller, if thedetected channel speed prediction information value is lower than apredetermined threshold value, controls a slope compensator to perform aslope compensation of a channel response signal of which a noisecomponent is eliminated by the first and second filter coefficients. 29.The apparatus as set forth in claim 27, wherein the noise level α iscalculated by the following equation:$\alpha = {\frac{1}{M_{pilot}}{\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)} - {\overset{\sim}{c}\left( {n - 1} \right)}}}}}$

where M_(pilot) is the number of pilot symbols for every packet, {tildeover (c)}(n) is the predicted channel response signal, and {tilde over(c)}(n−1) is the previously predicted channel response signal.
 30. Theapparatus as set forth in claim 27, wherein the channel speed predictioninformation is obtained by the following equations:$\beta = {\min \left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and min[x] is aminimum value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}{{{\overset{\sim}{c}(n)}} \cdot {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 31. The apparatus as set forth in claim 27,wherein the channel speed prediction information is obtained by thefollowing equations:$\beta = {{mean}\left\lbrack {{R_{\overset{\sim}{c}}(l)}/{\max \left( R_{\overset{\sim}{c}} \right)}} \right\rbrack}$

where β satisfies a predetermined condition of 0≦β≦1, and mean[x] is amean value of ‘x’; and${R_{\overset{\sim}{c}{(n)}}(l)} = {\sum\limits_{n = 1}^{M_{pilot}}\quad {{{\overset{\sim}{c}(n)}} \cdot \quad {{\overset{\sim}{c}\left( {n + l} \right)}}}}$

where $R_{\overset{\sim}{c}{(n)}}(l)$

is an auto-correlation function using the predicted channel responsesignal {tilde over (c)}(n).
 32. The apparatus as set forth in claim 28,wherein the slope compensator respectively assigns different channelcompensation values Δ(n) calculated by the following first equation to aplurality of data symbols corresponding to one pilot symbol, and adaptsthe assigned channel compensation values Δ(n) to the following secondequation, and performing slope compensation operations for every datasymbol, wherein said first equation is represented by:${\Lambda (n)} = {\frac{1}{W \cdot \left( {S\quad {F_{pilot}/S}\quad F_{data}} \right)}{\sum\limits_{w = 0}^{w - 1}\quad {{{\hat{c}\left( {n - w} \right)} - {\hat{c}\left( {n - w - 1} \right)}}}}}$

where n is an index of a pilot symbol contained in one packet, w is asize of window used for slope prediction, ĉ(n) is a signal of which anN-th noise is eliminated, SF_(pilot) is a spreading factor (SF) of apilot channel, SF_(date) is a spreading factor (SF) of a data channel,and SF_(pilot)/SF_(date) is the number of data symbols corresponding toone pilot symbol, wherein said second equation is represented by: c_(est)(n,k)=ĉ(n)·k·Λ(n) (1≦k≦SF _(pilot) /SF _(date)) where c_(est)(n,k)is a channel-compensated output signal of k-th data symbol with respectto an N-th pilot symbol.
 33. The apparatus as set forth in claim 27,wherein the filter coefficient controller equally distributes the secondfilter coefficient to N coefficients when the noise elimination filteris an N-th noise elimination filter.
 34. The apparatus as set forth inclaim 27, wherein the filter coefficient controller unequallydistributes the second filter coefficient to N coefficients due todifferent weights, when the noise elimination filter is an N-th noiseelimination filter.
 35. The apparatus as set forth in claim 27, whereinthe filter coefficient controller determines first and second filtercoefficients to be two filter coefficients of one group among aplurality of filter coefficient groups, each group being composed of twofilter coefficients, mapping-processed for every area differentlyassigned according to at least one first reference value fordiscriminating between noise levels and at least one second referencevalue for discriminating between channel speed prediction information,said selected one group being selectively determined according to thedetected noise level and the detected channel speed predictioninformation.